app.py

from dotenv import load_dotenv
from openai import OpenAI
import json
import os
import requests
from pypdf import PdfReader
import gradio as gr

# Load environment variables
load_dotenv(override=True)

def push(text):
    """Send push notification via Pushover"""
    response = requests.post(
        "https://api.pushover.net/1/messages.json",
        data={
            "token": os.getenv("PUSHOVER_TOKEN"),
            "user": os.getenv("PUSHOVER_USER"),
            "message": text,
        }
    )
    
    print("Pushover Response Code:", response.status_code)
    print("Pushover Response:", response.text)

def record_user_details(email, name="Name not provided", notes="not provided"):
    """Record user contact details and send push notification"""
    push(f"Recording interest from {name} with email {email} and notes {notes}")
    return {"recorded": "ok"}

def record_unknown_question(question):
    """Record questions that couldn't be answered"""
    push(f"Recording {question} asked that I couldn't answer")
    return {"recorded": "ok"}

# Tool definitions for OpenAI function calling
record_user_details_json = {
    "name": "record_user_details",
    "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
    "parameters": {
        "type": "object",
        "properties": {
            "email": {
                "type": "string",
                "description": "The email address of this user"
            },
            "name": {
                "type": "string",
                "description": "The user's name, if they provided it"
            },
            "notes": {
                "type": "string",
                "description": "Any additional information about the conversation that's worth recording to give context"
            }
        },
        "required": ["email"],
        "additionalProperties": False
    }
}

record_unknown_question_json = {
    "name": "record_unknown_question",
    "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
    "parameters": {
        "type": "object",
        "properties": {
            "question": {
                "type": "string",
                "description": "The question that couldn't be answered"
            },
        },
        "required": ["question"],
        "additionalProperties": False
    }
}

tools = [
    {"type": "function", "function": record_user_details_json},
    {"type": "function", "function": record_unknown_question_json}
]

class CareerBot:
    def __init__(self):
        self.openai = OpenAI()
        self.name = "Naresh"  # Change this to your name
        
        # Load LinkedIn profile from PDF
        try:
            reader = PdfReader("me/linkedin.pdf")
            self.linkedin = ""
            for page in reader.pages:
                text = page.extract_text()
                if text:
                    self.linkedin += text
        except FileNotFoundError:
            self.linkedin = "LinkedIn profile not available"
        
        # Load resume from PDF
        try:
            reader = PdfReader("me/NareshRajaML_AI_Role.pdf")  # Update filename
            self.resume_content = ""
            for page in reader.pages:
                text = page.extract_text()
                if text:
                    self.resume_content += text
        except FileNotFoundError:
            self.resume_content = "Resume not available"
        
        # Load summary text file
        try:
            with open("me/summary.txt", "r", encoding="utf-8") as f:
                self.summary = f.read()
        except FileNotFoundError:
            self.summary = "Professional summary not available"

    def handle_tool_calls(self, tool_calls):
        """Handle tool calls from OpenAI API"""
        results = []
        for tool_call in tool_calls:
            tool_name = tool_call.function.name
            arguments = json.loads(tool_call.function.arguments)
            print(f"Tool called: {tool_name}", flush=True)
            
            # Get the function from globals and execute it
            tool = globals().get(tool_name)
            result = tool(**arguments) if tool else {}
            
            results.append({
                "role": "tool",
                "content": json.dumps(result),
                "tool_call_id": tool_call.id
            })
        return results
    
    def get_system_prompt(self):
        """Generate the system prompt with context"""
        system_prompt = f"""You are acting as {self.name}. You are answering questions on {self.name}'s website, 
particularly questions related to {self.name}'s career, background, skills and experience. 
Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. 
You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. 
Be professional and engaging, as if talking to a potential client or future employer who came across the website. 
If you don't know the answer to any question, use your record_unknown_question tool to record the question that you couldn't answer, even if it's about something trivial or unrelated to career. 
If the user is engaging in discussion, try to steer them towards getting in touch via email; ask for their email and record it using your record_user_details tool."""

        system_prompt += f"\n\n## Summary:\n{self.summary}\n\n"
        system_prompt += f"## LinkedIn Profile:\n{self.linkedin}\n\n"
        system_prompt += f"## Resume Content:\n{self.resume_content}\n"
        system_prompt += f"Resume link: https://drive.google.com/file/d/1i8ChOcO0b1caSqE8i4CeiJduN9EbYLua/view?usp=sharing\n\n"
        system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
        
        return system_prompt
    
    def chat(self, message, history):
        """Main chat function for Gradio interface"""
        messages = [{"role": "system", "content": self.get_system_prompt()}] + history + [{"role": "user", "content": message}]
        
        done = False
        while not done:
            response = self.openai.chat.completions.create(
                model="gpt-4o-mini", 
                messages=messages, 
                tools=tools
            )
            
            if response.choices[0].finish_reason == "tool_calls":
                message_obj = response.choices[0].message
                tool_calls = message_obj.tool_calls
                results = self.handle_tool_calls(tool_calls)
                
                messages.append(message_obj)
                messages.extend(results)
            else:
                done = True
        
        return response.choices[0].message.content

# Initialize the bot
career_bot = CareerBot()

# Launch Gradio interface
if __name__ == "__main__":
    gr.ChatInterface(
        career_bot.chat, 
        type="messages",
        title=f"Chat with {career_bot.name}",
        description=f"Ask me about my professional background, experience, and career!"
    ).launch()